A compliance analyst asks which of the 10 CPMI API harmonisation recommendations specifically target commercial banks or correspondent banking institutions, which target payment system operators, which target central banks or regulators, and which target standards bodies—seeking a recommendation-by-recommendation stakeholder breakdown.
With no retrievable per-recommendation content, the model inferred stakeholder assignments from the recommendation category names and its knowledge of how standards-body governance typically works — BIAN, ISO, and SWIFT appear as plausible assignments to a harmonisation-processes category without any retrieved basis. The model presented this inference as a stakeholder breakdown, not as a reasoned extrapolation from category names.
Domain inference used as a stakeholder-assignment mechanism — assigning ISO, BIAN, and SWIFT to a harmonisation-processes category by structural reasoning — is not retrieval. The training data for this document appears to lack per-recommendation content, and the model's self-check did not flag that its output was constructed rather than retrieved. The RAG glue layer is not enforcing a 'content was found' gate before allowing domain-inference fill.
A business development team building a regulatory obligation matrix — mapping which CPMI recommendations apply to correspondent banks versus payment system operators versus central banks — would distribute incorrect stakeholder assignments if the AI's category-level framing is taken at face value. In an RFP or partnership proposal, this translates to mischaracterising the firm's product scope or the client's regulatory exposure. A central bank or FMI technology team reviewing that proposal will identify the error; the question is whether it disqualifies the bid or merely damages the relationship.
Each finding has a stable Citation ID (RLB-F-… for aggregated case-study findings, RLB-H-… for raw per-model hallucinations) — like a DOI, the ID always resolves to the canonical finding even if URLs change.
RegLeg Specialist Panel (2026). "Finding#2 — Invented per-recommendation stakeholder targeting assignments — Software Saas × Product Bizdev — International / Multilateral." Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008. RegLegBrief AI Hallucination Research, published 2026-06-04. https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/software_saas/product_bizdev/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/
RegLeg Specialist Panel. (2026). Finding#2 — Invented per-recommendation stakeholder targeting assignments [Hallucination finding RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008]. RegLegBrief AI Hallucination Research. https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/software_saas/product_bizdev/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/
RegLeg Specialist Panel, Finding#2 — Invented per-recommendation stakeholder targeting assignments [RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008], RegLegBrief AI Hallucination Research (June 04, 2026), https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/software_saas/product_bizdev/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/.
@misc{reglegbrief_RLB_F_INT_BIS_CPMI_API_HARMONISATION_CROSS_BORDER_2024_Q008,
author = {RegLeg Specialist Panel},
title = {Finding#2 — Invented per-recommendation stakeholder targeting assignments},
year = {2026},
publisher = {RegLegBrief AI Hallucination Research},
note = {Hallucination finding Citation ID: RLB-F-INT-BIS-CPMI-API-HARMONISATION-CROSS-BORDER-2024-Q008},
url = {https://reglegbrief.com/regulators/j1/int/bis-cpmi/cpmi-api-harmonisation-cross-border-2024/sectors/software_saas/product_bizdev/finding/INT-BIS-CPMI-INT-001-CPMI-API-HARMONISATION-CROSS-BORDER-2024-v1-008/}
}